Deep labeller: automatic bounding box generation for synthetic violence detection datasets

Nadeem, Shahroz, Kurugollu, Fatih, Saravi, Sara, Atlam, Hany F. and Franqueira, Virginia N. L. (2024) Deep labeller: automatic bounding box generation for synthetic violence detection datasets. Multimedia Tools and Applications, 83 (4). pp. 10717-10734. ISSN 1380-7501

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Manually labelling datasets for training violence detection systems is time-consuming, expensive, and labor-intensive. Mind wandering, boredom, and short attention span can also cause labelling errors. Moreover, collecting and distributing sensitive images containing violence has ethical implications. Automation is the future for labelling sensitive image datasets. Deep labeller is a two-stage Deep Learning (DL) method that uses pre-trained DL object detection methods on MS-COCO for automatic labelling. The Deep Labeller method labels violent and nonviolent images in WVD and USI. In stage 1, WVD generates weak labels using synthetic images. In stage 2, the Deep labeller method is retrained on weak labels. USI dataset is used to test our method on real-world violence. Deep labeller generated weak and strong labels with an IoU of 0.80036 in stage 1 and 0.95 in stage 2 on the WVD. Automatically generated labels. To test our method’s generalisation power, violent and nonviolent image labels on USI dataset had a mean IoU of 0.7450.

Item Type: Article
Uncontrolled Keywords: data labelling, violence detection, WVD, USI, synthetic virtual violence
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Health & Science > Department of Science & Technology
Depositing User: David Upson-Dale
Date Deposited: 24 Apr 2024 09:37
Last Modified: 24 Apr 2024 09:37

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